Complexity-Efficient Dependent Quantization for Versatile Video Coding

The dependent quantization (DQ) technique in Versatile Video Coding (VVC) provides high compression efficiency, but it suffers from much computation, which is not preferred in practical applications. In this context, we propose a complexity-efficient dependent quantization structure employing a machine learning-based DQ start position prediction and a simplified trellis structure. Firstly, the proposed DQ start position is decided by a binary classifier which finds an appropriate location to start the DQ process. To further reduce the processing time of the binary classification, we also set up a precondition with a learned preset threshold which can reduce the number of coefficients subjected to the classifier. Based on our statistical analysis showing that under certain conditions some branches of the trellis structure are not necessary to check, we also design a branch pruning method and a removal scheme of all-zero coefficient group checking to simplify the DQ trellis structure. Our proposed complexity-efficient DQ has much fewer calculations of rate-distortion costs. Our experimental results show that it can reduce the computational complexity of the DQ process with nearly no performance loss of Bjøntegaard delta bit rate (BDBR) irrespective of the all intra or the random access configurations.